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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A Generalized Sampling and Preconditioning Scheme for Sparse Approximation of Polynomial Chaos Expansions

We propose an algorithm for recovering sparse orthogonal polynomial expansions via collocation. A standard sampling approach for recovering sparse polynomials uses Monte Carlo sampling, from the density of orthogonality, which results in poor function recovery when the polynomial degree is high. Our proposed approach aims to mitigate this limitation by sampling with respect to the weighted equilibrium measure of the parametric domain and subsequently solves a preconditioned $\ell^1$-minimization problem, where the weights of the diagonal preconditioning matrix are given by evaluations of the Christoffel function. Our algorithm can be applied to a wide class of orthogonal polynomial families on bounded and unbounded domains, including all classical families. We present theoretical analysis to motivate the algorithm and numerical results that show our method is superior to standard Monte Carlo methods in many situations of interest. In conclusion, numerical examples are also provided to demonstrate that our proposed algorithm leads to comparable or improved accuracy even when compared with Legendre- and Hermite-specific algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

TH-E-18A-01: Developments in Monte Carlo Methods for Medical Imaging

Monte Carlo simulation methods are widely used in medical physics research and are starting to be implemented in clinical applications such as radiation therapy planning systems. Monte Carlo simulations offer the capability to accurately estimate quantities of interest that are challenging to measure experimentally while taking into account the realistic anatomy of an individual patient. Traditionally, practical application of Monte Carlo simulation codes in diagnostic imaging was limited by the need for large computational resources or long execution times. However, recent advancements in high-performance computing hardware, combined with a new generation of Monte Carlo simulation algorithms and novel postprocessing methods, are allowing for the computation of relevant imaging parameters of interest such as patient organ doses and scatter-to-primaryratios in radiographic projections in just a few seconds using affordable computational resources. Programmable Graphics Processing Units (GPUs), for example, provide a convenient, affordable platform for parallelized Monte Carlo executions that yield simulation times on the order of 10{sup 7} xray/ s. Even with GPU acceleration, however, Monte Carlo simulation times can be prohibitive for routine clinical practice. To reduce simulation times further, variance reduction techniques can be used to alter the probabilistic models underlying the x-ray tracking process, resulting in lower variance in the results without biasing the estimates. Other complementary strategies for further reductions in computation time are denoising of the Monte Carlo estimates and estimating (scoring) the quantity of interest at a sparse set of sampling locations (e.g. at a small number of detector pixels in a scatter simulation) followed by interpolation. Beyond reduction of the computational resources required for performing Monte Carlo simulations in medical imaging, the use of accurate representations of patient anatomy is crucial to the virtual generation of medical images and accurate estimation of radiation dose and other imaging parameters. For this, detailed computational phantoms of the patient anatomy must be utilized and implemented within the radiation transport code. Computational phantoms presently come in one of three format types, and in one of four morphometric categories. Format types include stylized (mathematical equation-based), voxel (segmented CT/MR images), and hybrid (NURBS and polygon mesh surfaces). Morphometric categories include reference (small library of phantoms by age at 50th height/weight percentile), patient-dependent (larger library of phantoms at various combinations of height/weight percentiles), patient-sculpted (phantoms altered to match the patient's unique outer body contour), and finally, patient-specific (an exact representation of the patient with respect to both body contour and internal anatomy). The existence and availability of these phantoms represents a very important advance for the simulation of realistic medical imaging applications using Monte Carlo methods. New Monte Carlo simulation codes need to be thoroughly validated before they can be used to perform novel research. Ideally, the validation process would involve comparison of results with those of an experimental measurement, but accurate replication of experimental conditions can be very challenging. It is very common to validate new Monte Carlo simulations by replicating previously published simulation results of similar experiments. This process, however, is commonly problematic due to the lack of sufficient information in the published reports of previous work so as to be able to replicate the simulation in detail. To aid in this process, the AAPM Task Group 195 prepared a report in which six different imaging research experiments commonly performed using Monte Carlo simulations are described and their results provided. The simulation conditions of all six cases are provided in full detail, with all necessary data on material composition, source, geometry, scoring and other parameters provided. The results of these simulations when performed with the four most common publicly available Monte Carlo packages are also provided in tabular form. The Task Group 195 Report will be useful for researchers needing to validate their Monte Carlo work, and for trainees needing to learn Monte Carlo simulation methods. In this symposium we will review the recent advancements in highperformance computing hardware enabling the reduction in computational resources needed for Monte Carlo simulations in medical imaging. We will review variance reduction techniques commonly applied in Monte Carlo simulations of medical imaging systems and present implementation strategies for efficient combination of these techniques with GPU acceleration. Trade-offs involved in Monte Carlo acceleration by means of denoising and “sparse sampling” will be discussed. A method for rapid scatter correction in cone-beam CT (<5 min/scan) will be presented as an illustration of the simulation speeds achievable with optimized Monte Carlo simulations. We will also discuss the development, availability, and capability of the various combinations of computational phantoms for Monte Carlo simulation of medical imaging systems. Finally, we will review some examples of experimental validation of Monte Carlo simulations and will present the AAPM Task Group 195 Report. Learning Objectives: Describe the advances in hardware available for performing Monte Carlo simulations in high performance computing environments. Explain variance reduction, denoising and sparse sampling techniques available for reduction of computational time needed for Monte Carlo simulations of medical imaging. List and compare the computational anthropomorphic phantoms currently available for more accurate assessment of medical imaging parameters in Monte Carlo simulations. Describe experimental methods used for validation of Monte Carlo simulations in medical imaging. Describe the AAPM Task Group 195 Report and its use for validation and teaching of Monte Carlo simulations in medical imaging.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Plug-and-Play Priors for Bright Field Electron Tomography and Sparse Interpolation

Many material and biological samples in scientific imaging are characterized by nonlocal repeating structures. These are studied using scanning electron microscopy and electron tomography. Sparse sampling of individual pixels in a two-dimensional image acquisition geometry, or sparse sampling of projection images with large tilt increments in a tomography experiment, can enable high speed data acquisition and minimize sample damage caused by the electron beam. In this paper, we present an algorithm for electron tomographic reconstruction and sparse image interpolation that exploits the nonlocal redundancy in images. We adapt a framework, termed plug-and-play priors, to solve these imaging problems in a regularized inversion setting. The power of the plug-and-play approach is that it allows a wide array of modern denoising algorithms to be used as a “prior model” for tomography and image interpolation. We also present sufficient mathematical conditions that ensure convergence of the plug-and-play approach, and we use these insights to design a new nonlocal means denoising algorithm. Finally, we demonstrate that the algorithm produces higher quality reconstructions on both simulated and real electron microscope data, along with improved convergence properties compared to other methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2015

Application of High Performance Computing for Studying Cyclic Variability in Dilute Internal Combustion Engines

Combustion instabilities in dilute internal combustion engines are manifest in cyclic variability (CV) in engine performance measures such as integrated heat release or shaft work. Understanding the factors leading to CV is important in model-based control, especially with high dilution where experimental studies have demonstrated that deterministic effects can become more prominent. Observation of enough consecutive engine cycles for significant statistical analysis is standard in experimental studies but is largely wanting in numerical simulations because of the computational time required to compute hundreds or thousands of consecutive cycles. We have proposed and begun implementation of an alternative approach to allow rapid simulation of long series of engine dynamics based on a low-dimensional mapping of ensembles of single-cycle simulations which map input parameters to output engine performance. This paper details the use Titan at the Oak Ridge Leadership Computing Facility to investigate CV in a gasoline direct-injected spark-ignited engine with a moderately high rate of dilution achieved through external exhaust gas recirculation. The CONVERGE™ CFD software was used to perform single-cycle simulations with imposed variations of operating parameters and boundary conditions selected according to a sparse grid sampling of the parameter space. Using an uncertainty quantification technique, the sampling scheme is chosen similar to a design of experiments grid but uses algorithms designed to minimize the number of samples required to achieve a desired degree of accuracy. The simulations map input parameters to output metrics of engine performance for a single cycle, and by mapping over a large parameter space, results can be interpolated from within that space. This interpolation scheme forms the basis for a low-dimensional ‘metamodel’ (or model of a model) which can be used to mimic the dynamical behavior of corresponding high-dimensional simulations. Simulations of high-EGR spark-ignition combustion cycles within a parametric sampling grid were performed and analyzed statistically, and sensitivities of the physical factors leading to high CV are presented. With these results, the prospect of producing low-dimensional metamodels to describe engine dynamics at any point in the parameter space will be discussed. Additionally, modifications to the methodology to account for nondeterministic effects in the numerical solution environment are proposed.

Finney, Charles E. A.↗
DOE OSTI2026

Improving statistical precision in Monte Carlo samples with negative weights via reweighting and uncertainty quantification

High statistical precision is critical for Monte Carlo (MC) samples in high energy physics and is degraded by negatively weighted events. This paper investigates a procedure to learn the relationship between the negative and positive weight distributions of any sample, allowing the reduction of statistical uncertainty by reweighting kinematically equivalent events with the same sign. A robust uncertainty quantification method is required for the practical application of such method. Two methods for the estimation of the reweighting uncertainty are developed: one at the event and another one at the final observable level. The latter method is strongly favored. The gains in statistical precision are then quantified. The method is demonstrated on Sherpa vector boson plus jets samples when using all generated events and when restricted to the signal region of a mock analysis. It is demonstrated to significantly reduce stochastic behavior in sparse MC samples while decreasing the overall uncertainty with a sufficiently well-known reweighting function.

Monte Carlo methods↗
DOE OSTI2013

Mineral Association Changes the Secondary Structure and Dynamics of Murine Amelogenin

Biomineralization proteins, present during the formation of hard tissues including bones, teeth, egg shells and nacre, result in the exquisite structures and properties of the resulting materials.[1] The structure of these proteins is often implicated in the control of the mineral properties, however very little structural data is available for the bulk of these proteins due to the difficulty in determining structures of immobilized proteins. Solid-state NMR is uniquely suited to the study of the structure of proteins bound to surfaces, demonstrated with the structural and orientation insights provided for the hydroxyapatite mineralization proteins statherin and the amelogenin, LRAP.[2] While these data are some of the only structural data available for this important class of protein, the experiments are often expensive and time consuming, due to the need to prepare and measure samples with isolated spin pairs, and are limited to a size of ~60 residues. In this work, we utilized a combination of 1D and recent 2D[3] solid-state NMR techniques along with a sparsely labelled sample to characterize the structure and dynamics of potential HAP binding residues of the 180 residue enamel protein, amelogenin. Amelogenin nanospheres and mineral bound amelogenin were investigated and a shift from unstructured to β-sheet structure was observed, along with a decrease in protein flexibility. This work provides the first molecular level structure and dynamic information of full-length amelogenin on the surface of hydroxyapatite (HAP) and within nanospheres, and demonstrates the ability to evaluate structural characteristics of large biomineralization proteins bound to their physiologically relevant surface. The research was performed at the Pacific Northwest National Laboratory (PNNL), a facility operated by Battelle for the U.S. Department of Energy, with a portion of it performed at the W.R. Wiley Environmental Molecular Sciences Laboratory (EMSL), a national scientific user facility sponsored by the U.S. DOE Biological and Environmental Research program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Randomized methods to characterize large-scale vortical flow networks

We demonstrate the effective use of randomized methods for linear algebra to perform network-based analysis of complex vortical flows. Network theoretic approaches can reveal the connectivity structures among a set of vortical elements and analyze their collective dynamics. These approaches have recently been generalized to analyze high-dimensional turbulent flows, for which network computations can become prohibitively expensive. In this work, we propose efficient methods to approximate network quantities, such as the leading eigendecomposition of the adjacency matrix, using randomized methods. Specifically, we use the Nyström method to approximate the leading eigenvalues and eigenvectors, achieving significant computational savings and reduced memory requirements. The effectiveness of the proposed technique is demonstrated on two high-dimensional flow fields: two-dimensional flow past an airfoil and two-dimensional turbulence. We find that quasi-uniform column sampling outperforms uniform column sampling, while both feature the same computational complexity.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Sparsifying priors for Bayesian uncertainty quantification in model discovery

We propose a probabilistic model discovery method for identifying ordinary differential equations governing the dynamics of observed multivariate data. Our method is based on the sparse identification of nonlinear dynamics (SINDy) framework, where models are expressed as sparse linear combinations of pre-specified candidate functions. Promoting parsimony through sparsity leads to interpretable models that generalize to unknown data. Instead of targeting point estimates of the SINDy coefficients, we estimate these coefficients via sparse Bayesian inference. The resulting method, uncertainty quantification SINDy (UQ-SINDy), quantifies not only the uncertainty in the values of the SINDy coefficients due to observation errors and limited data, but also the probability of inclusion of each candidate function in the linear combination. UQ-SINDy promotes robustness against observation noise and limited data, interpretability (in terms of model selection and inclusion probabilities) and generalization capacity for out-of-sample forecast. Sparse inference for UQ-SINDy employs Markov chain Monte Carlo, and we explore two sparsifying priors: the spike and slab prior, and the regularized horseshoe prior. UQ-SINDy is shown to discover accurate models in the presence of noise and with orders-of-magnitude less data than current model discovery methods, thus providing a transformative method for real-world applications which have limited data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Do Diurnal Aerosol Changes Affect Daily Average Radiative Forcing?

Strong diurnal variability of aerosol has been observed frequently for many urban/industrial regions. How this variability may alter the direct aerosol radiative forcing (DARF), however, is largely unknown. To quantify changes in the time-averaged DARF, we perform an assessment of 29 days of high temporal resolution ground-based data collected during the Two-Column Aerosol Project (TCAP) on Cape Cod, which is downwind of metropolitan areas. We demonstrate that strong diurnal changes of aerosol loading (about 20% on average) have a negligible impact on the 24-h average DARF, when daily averaged optical properties are used to find this quantity. However, when there is a sparse temporal sampling of aerosol properties, which may preclude the calculation of daily averaged optical properties, large errors (up to 100%) in the computed DARF may occur. We describe a simple way of reducing these errors, which suggests the minimal temporal sampling needed to accurately find the forcing.

aerosol↗
DOE OSTI2024

Collocation methods for nonlinear differential equations on low-rank manifolds

We introduce new methods for integrating nonlinear differential equations on low-rank manifolds. These methods rely on interpolatory projections onto the tangent space, enabling low-rank time integration of vector fields that can be evaluated entry-wise. A key advantage of our approach is that it does not require the vector field to exhibit low-rank structure, thereby overcoming significant limitations of traditional dynamical low-rank methods based on orthogonal projection. To construct the interpolatory projectors, we develop a sparse tensor sampling algorithm based on the discrete empirical interpolation method (DEIM) that parameterizes tensor train manifolds and their tangent spaces with cross interpolation. Using these projectors, we propose two time integration schemes on low-rank tensor train manifolds. The first scheme integrates the solution at selected interpolation indices and constructs the solution with cross interpolation. The second scheme generalizes the well-known orthogonal projector-splitting integrator to interpolatory projectors. We demonstrate the proposed methods with applications to several tensor differential equations arising from the discretization of partial differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

Model-based analysis of thromboxane B{sub 2} and prostaglandin E{sub 2} as biomarkers in the safety evaluation of naproxen

The assessment of safety in traditional toxicology protocols relies on evidence arising from observed adverse events (AEs) in animals and on establishing their correlation with different measures of drug exposure (e.g., C{sub max} and AUC). Such correlations, however, ignore the role of biomarkers, which can provide further insight into the underlying pharmacological mechanisms. Here we use naproxen as a paradigm drug to explore the feasibility of a biomarker-guided approach for the prediction of AEs in humans. A standard toxicology protocol was set up for the evaluation of effects of naproxen in rat, in which four doses were tested (7.5, 15, 40 and 80 mg/kg). In addition to sparse blood sampling for the assessment of exposure, thromboxane B{sub 2} and prostaglandin E{sub 2} were also collected in satellite groups. Nonlinear mixed effects modelling was used to evaluate the predictive performance of the approach. A one-compartmental model with first order absorption was found to best describe the pharmacokinetics of naproxen. A nonlinear relationship between dose and bioavailability was observed which leads to a less than proportional increase in naproxen concentrations with increasing doses. The pharmacodynamics of TXB{sub 2} and PGE{sub 2} was described by direct inhibition models with maximum pharmacological effects achieved at doses > 7.5 mg/kg. The predicted PKPD relationship in humans was within 10-fold of the values previously published. Moreover, our results indicate that biomarkers can be used to assess interspecies differences in PKPD and extrapolated data from animals to humans. Biomarker sampling should be used systematically in general toxicity studies. - Highlights: • Prediction of a drug's safety profile from preclinical protocols remains challenging. • Pharmacokinetic measures of safe exposure (e.g., AUC) ignore the role of biomarkers. • PKPD relationships enable the evaluation of adverse events in a mechanistic manner. • Major differences exist between rats and humans in the effects of naproxen on TXB{sub 2}. • A biomarker-guided approach may facilitate the prediction of adverse events in humans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Observations of Ocean Surface Wave Attenuation in Sea Ice Using Seafloor Cables

Abstract The attenuation of ocean surface waves during seasonal ice cover is an important control on the evolution of Arctic coastlines. The spatial and temporal variations in this process have been challenging to resolve with conventional sampling using sparse arrays of moorings or buoys. We demonstrate a novel method for persistent observation of wave‐ice interactions using distributed acoustic sensing (DAS) along existing seafloor fiber optic telecommunications cables. DAS measurements span a 36‐km cross‐shore cable on the Beaufort Shelf from Oliktok Point, Alaska. DAS optical sensing of fiber strain‐rate provides a proxy for seafloor pressure, which we calibrate with wave buoy measurements during the ice‐free season (August 2022). We apply this calibration during the ice formation season (November 2021) to obtain unprecedented resolution of variable wave attenuation rates in new, partial ice cover. The location and strength of wave attenuation serve as proxies for ice coverage and thickness, especially during rapidly evolving events.

Smith, Madison M.↗
DOE OSTI2012

SUBMILLIMETER LINE SPECTRUM OF THE SEYFERT GALAXY NGC 1068 FROM THE HERSCHEL-SPIRE FOURIER TRANSFORM SPECTROMETER

The first complete submillimeter spectrum (190-670 {mu}m) of the Seyfert 2 galaxy NGC 1068 has been observed with the SPIRE Fourier transform spectrometer on board the Herschel Space Observatory. The sequence of CO lines (J {sub up} = 4-13), lines from H{sub 2}O, the fundamental rotational transition of hydrogen fluoride, two o-H{sub 2}O{sup +} lines, and one line each from CH{sup +} and OH{sup +} have been detected, together with the two [C I] lines and the [N II] 205 {mu}m line. The observations in both single pointing mode with sparse image sampling and in mapping mode with full image sampling allow us to disentangle two molecular emission components, one due to the compact circumnuclear disk (CND) and one from the extended region encompassing the star-forming ring (SF-ring). Radiative transfer models show that the two CO components are characterized by densities of n(H{sub 2}) = 10{sup 4.5} and 10{sup 2.9} cm{sup -3} and temperatures of T {sub kin} = 100 K and 127 K, respectively. A comparison of the CO line intensities with the photodissociation region (PDR) and X-ray-dominated region (XDR) models, together with the other observational constraints, such as the observed CO surface brightness and the radiation field, indicates that the best explanation for the CO excitation of the CND is an XDR with a density of n(H{sub 2}) {approx} 10{sup 4} cm{sup -3} and an X-ray flux of 9 erg s{sup -1} cm{sup -2}, consistent with illumination by the active galactic nucleus, while the CO lines in the SF-ring are better modeled by a PDR. The detected water transitions, together with those observed with the Herschel PACS spectrometer, can be modeled by a large velocity gradient model with low temperature (T {sub kin} {approx} 40 K) and high density (n(H{sub 2}) in the range 10{sup 6.7}-10{sup 7.9} cm{sup -3}). The emission of H{sub 2}O{sup +} and OH{sup +} are in agreement with PDR models with cosmic-ray ionization. The diffuse ionized atomic component observed through the [N II] 205 {mu}m line is consistent with previous photoionization models of the starburst.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Adaptive X-ray imaging with reinforcement learning

X-ray imaging is a powerful technique to scan samples in a variety of contexts including biological, environmental and materials science, but commonly requires a synchrotron light source to produce X-rays at sufficient intensity. As these facilities are expensive to operate, the available beam time is limited and always in high demand. Particularly if the illuminated samples are sparse, standard raster scanning methods can be time-consuming, with a majority of that time being spent on areas of the image that carry little information. To increase the efficiency and maximize the information gain for a given time budget, we split the scanning process into a series of steps where previous measurements are used to inform the decision making and adapt the exposure distribution at later stages of the sequence. We formulate this task as a reinforcement learning problem where the goal is to produce a sequence of exposure maps that maximize a predefined scalar metric. We demonstrate the potential of this approach in simulations where the adaptive illumination can accelerate the measurement process by up to an order of magnitude compared with standard raster scanning. Finally, we present the first results from deploying the trained agents on an X-ray fluorescence beamline at the Stanford Synchrotron Radiation Lightsource.

Reinforcement Learning↗
DOE OSTI2022

Code for paper "Reconstructing an Implosion with Space-Time Smoothing of Velocimetry Traces"

New diagnostics to obtain high-temporal resolution observation of surface velocity provide a rich collection of data from high-explosive (HE) experiments. Using this data to estimate the temporal evolution of an imploding, HE-driven surface is of great interest for validating simulation of the physical system and for design of future experiments. This is the code for a paper in which we propose a method for using a Gaussian process to estimate the surface position over time and provide uncertainty estimates on that reconstruction accounting for measurement error, uncertainty to sparse spatial sampling velocity data, and robustness to outlying observations. In the paper, the code is applied on data from a hemispherical HE implosion experiment. This code is for release with the paper for peer-review, replicating the results and making the figures from the paper.

Grosskopf, Michael↗
DOE OSTI2025

Evaluation of Hypotheses for the Fate of Historical Discharges from Hanford Site BC Cribs and Trenches

A preliminary review of declassified reports and historical documents from the 1950s and 1960s indicated they may contain valuable information regarding potential impacts to groundwater from liquid waste discharged to the 216-BC Cribs and Trenches during the late 1950s. Transport of contaminants in the vadose zone beneath the BC Cribs area is simulated by a vadose zone model developed for the Composite Analysis (DOE/RL-2019-52, Composite Analysis for Low-Level Waste Disposal in the Hanford Site Central Plateau ). Results from this model indicate there has been no substantial breakthrough of contaminants (particularly technetium-99) to groundwater from the vadose zone up to the present day. However, there is uncertainty in the anisotropy and other model input parameters as well as acknowledged sparseness of sampling data for this area. In this study, historical documents and other information were reviewed to evaluate the possibility that contaminant breakthrough to groundwater occurred during or shortly after operation of the BC Cribs sites, and whether refinements of the vadose zone model parametrization should be considered.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗